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GTC ON-DEMAND

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Abstract:
We''ll present a framework that can learn a compute-intensive deep neural networks (DNNs) task using multiple AI blocks and evolve better confidence by combining estimates. We''ll consider the example of establishing the identity of a user using speech and image data. The system consists of two blocks - the AI block and Arbiter block. The AI block uses multiple DNNs (voice-based and image-based DNNs that generate a low confidence estimate initially). These AI blocks assist each other using Arbiter blocks and build confidence, improve accuracy, and learn salient features over time. Arbiter can store recent unacquainted data at run time in noisy and distorted environments and train the AI blocks periodically or on an on-demand basis. This concept could potentially improve the automatic speech recognition capabilities and allow detection of faces even when variable features of faces change with time. The GPU is the ideal choice as the task requires inferencing as well as training on the go.
We''ll present a framework that can learn a compute-intensive deep neural networks (DNNs) task using multiple AI blocks and evolve better confidence by combining estimates. We''ll consider the example of establishing the identity of a user using speech and image data. The system consists of two blocks - the AI block and Arbiter block. The AI block uses multiple DNNs (voice-based and image-based DNNs that generate a low confidence estimate initially). These AI blocks assist each other using Arbiter blocks and build confidence, improve accuracy, and learn salient features over time. Arbiter can store recent unacquainted data at run time in noisy and distorted environments and train the AI blocks periodically or on an on-demand basis. This concept could potentially improve the automatic speech recognition capabilities and allow detection of faces even when variable features of faces change with time. The GPU is the ideal choice as the task requires inferencing as well as training on the go.  Back
 
Topics:
AI & Deep Learning Research, Intelligent Video Analytics, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8331
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Abstract:
Driver monitoring systems are used to detect many driver attributes like gaze, head pose, eye openness, and other features pertaining to attention and assistance. We''ll present a synthetic method of generating data for training DNNs, which caters to the above mentioned features of the subject. We use blender for generating synthetic images, powered by NVIDIA GPUs, which can be scaled to match training needs. Synthetic data generatation allows precise control over data points that are difficult to control in a real environment, like pupil dialation. This approach avoids noisy measurements and results in high accuracy without the need for a high-precision 3D sensor.
Driver monitoring systems are used to detect many driver attributes like gaze, head pose, eye openness, and other features pertaining to attention and assistance. We''ll present a synthetic method of generating data for training DNNs, which caters to the above mentioned features of the subject. We use blender for generating synthetic images, powered by NVIDIA GPUs, which can be scaled to match training needs. Synthetic data generatation allows precise control over data points that are difficult to control in a real environment, like pupil dialation. This approach avoids noisy measurements and results in high accuracy without the need for a high-precision 3D sensor.  Back
 
Topics:
AI & Deep Learning Research, Autonomous Vehicles, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8324
Streaming:
Download:
Share:
 
 
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